Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "40" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 27 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 27 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460010 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460009 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460008 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460007 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | digital_ok | 0.00% | 99.92% | 99.92% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0832 | 0.1331 | 0.0407 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.313568 | 0.421551 | 1.879409 | 2.214467 | -0.043783 | 1.014994 | 1.698470 | 69.463698 | 0.5978 | 0.6009 | 0.3761 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.425210 | 0.842947 | 9.792773 | 0.888369 | 7.707361 | 0.127183 | 2.204801 | 0.744308 | 0.0388 | 0.6224 | 0.4704 | nan | nan |
| 2459996 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.449631 | 1.485743 | 12.312785 | 1.138282 | 7.297242 | 0.469523 | 0.403830 | 4.933371 | 0.0371 | 0.6204 | 0.4672 | nan | nan |
| 2459995 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.668159 | 1.646254 | 11.396728 | 0.698100 | 8.026618 | 0.368475 | 0.265185 | 2.940070 | 0.0421 | 0.6110 | 0.4585 | nan | nan |
| 2459994 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.269017 | 0.749128 | 9.828079 | 0.834994 | 7.760575 | -0.352301 | 0.157402 | -0.137934 | 0.0372 | 0.6082 | 0.4558 | nan | nan |
| 2459993 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.417085 | 1.325360 | 9.114100 | 0.597572 | 10.145651 | -0.187892 | 0.700631 | 0.988320 | 0.0318 | 0.6068 | 0.4282 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.231622 | 0.557571 | 9.669812 | 0.665782 | 9.141904 | -0.459918 | 0.162075 | 0.718013 | 0.0361 | 0.6173 | 0.4675 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.904020 | 0.551383 | 9.468775 | 0.566370 | 9.123224 | -0.087397 | 2.386363 | 1.969859 | 0.0388 | 0.6155 | 0.4680 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.700626 | 0.837433 | 8.427050 | 0.745940 | 8.068310 | 0.323502 | 2.598632 | 2.478227 | 0.0356 | 0.6119 | 0.4686 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.604216 | 0.436627 | 9.765508 | 0.506479 | 10.774163 | -0.644178 | 0.785254 | 0.722443 | 0.0352 | 0.6098 | 0.4698 | nan | nan |
| 2459987 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.514758 | 0.391181 | 9.479685 | 0.721852 | 6.387683 | -0.355458 | 1.388345 | 0.305915 | 0.0388 | 0.6226 | 0.4758 | nan | nan |
| 2459986 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.959282 | 0.739496 | 10.383730 | 0.621284 | 9.370044 | -0.698955 | 5.632668 | -0.223490 | 0.0371 | 0.6459 | 0.4961 | nan | nan |
| 2459985 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.946807 | 2.153404 | 9.626215 | 0.650129 | 7.256110 | 0.086834 | 3.105009 | 2.588027 | 0.0368 | 0.6111 | 0.4713 | nan | nan |
| 2459984 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.408194 | 0.827301 | 9.992239 | 0.706984 | 9.605698 | 0.333771 | 4.206071 | 1.935483 | 0.0400 | 0.6358 | 0.4847 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.186762 | 4.425444 | 9.541620 | 0.492294 | 9.274287 | -0.505292 | 2.824126 | -0.435668 | 0.0384 | 0.6396 | 0.4752 | nan | nan |
| 2459982 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 8.611296 | 2.685795 | 8.097874 | 0.645887 | 4.519640 | -0.221137 | 2.456004 | 0.092691 | 0.0369 | 0.6867 | 0.5151 | nan | nan |
| 2459981 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.527792 | 3.553462 | 10.158872 | 0.403067 | 10.420064 | -0.487298 | 0.442990 | 0.483414 | 0.0391 | 0.6111 | 0.4676 | nan | nan |
| 2459980 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.320872 | 1.657892 | 9.132053 | 0.462202 | 9.048397 | -0.675973 | 5.268629 | 0.339334 | 0.0386 | 0.6639 | 0.5022 | nan | nan |
| 2459979 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.721907 | 3.637955 | 8.446949 | 0.390354 | 8.922463 | -0.259681 | 0.330876 | 0.406799 | 0.0382 | 0.6068 | 0.4709 | nan | nan |
| 2459978 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.816976 | 2.750347 | 9.178747 | 0.391082 | 9.341094 | -0.797772 | 0.975727 | 0.539482 | 0.0348 | 0.6087 | 0.4714 | nan | nan |
| 2459977 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.152766 | 3.485694 | 9.028112 | 0.426306 | 9.313096 | 0.488296 | 1.326138 | 1.359330 | 0.0400 | 0.5705 | 0.4303 | nan | nan |
| 2459976 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.037862 | 4.293943 | 9.494251 | 0.382557 | 9.403131 | 0.262817 | 1.203496 | 0.121737 | 0.0361 | 0.6144 | 0.4714 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | nn Temporal Discontinuties | 69.463698 | -0.313568 | 0.421551 | 1.879409 | 2.214467 | -0.043783 | 1.014994 | 1.698470 | 69.463698 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Power | 9.792773 | 9.425210 | 0.842947 | 9.792773 | 0.888369 | 7.707361 | 0.127183 | 2.204801 | 0.744308 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Power | 12.312785 | 10.449631 | 1.485743 | 12.312785 | 1.138282 | 7.297242 | 0.469523 | 0.403830 | 4.933371 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Power | 11.396728 | 10.668159 | 1.646254 | 11.396728 | 0.698100 | 8.026618 | 0.368475 | 0.265185 | 2.940070 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 10.269017 | 10.269017 | 0.749128 | 9.828079 | 0.834994 | 7.760575 | -0.352301 | 0.157402 | -0.137934 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 11.417085 | 11.417085 | 1.325360 | 9.114100 | 0.597572 | 10.145651 | -0.187892 | 0.700631 | 0.988320 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 12.231622 | 12.231622 | 0.557571 | 9.669812 | 0.665782 | 9.141904 | -0.459918 | 0.162075 | 0.718013 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 9.904020 | 0.551383 | 9.904020 | 0.566370 | 9.468775 | -0.087397 | 9.123224 | 1.969859 | 2.386363 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 9.700626 | 0.837433 | 9.700626 | 0.745940 | 8.427050 | 0.323502 | 8.068310 | 2.478227 | 2.598632 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 11.604216 | 0.436627 | 11.604216 | 0.506479 | 9.765508 | -0.644178 | 10.774163 | 0.722443 | 0.785254 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 9.514758 | 9.514758 | 0.391181 | 9.479685 | 0.721852 | 6.387683 | -0.355458 | 1.388345 | 0.305915 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 11.959282 | 0.739496 | 11.959282 | 0.621284 | 10.383730 | -0.698955 | 9.370044 | -0.223490 | 5.632668 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 10.946807 | 2.153404 | 10.946807 | 0.650129 | 9.626215 | 0.086834 | 7.256110 | 2.588027 | 3.105009 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 10.408194 | 10.408194 | 0.827301 | 9.992239 | 0.706984 | 9.605698 | 0.333771 | 4.206071 | 1.935483 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 10.186762 | 10.186762 | 4.425444 | 9.541620 | 0.492294 | 9.274287 | -0.505292 | 2.824126 | -0.435668 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 8.611296 | 8.611296 | 2.685795 | 8.097874 | 0.645887 | 4.519640 | -0.221137 | 2.456004 | 0.092691 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Temporal Variability | 10.420064 | 3.553462 | 9.527792 | 0.403067 | 10.158872 | -0.487298 | 10.420064 | 0.483414 | 0.442990 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 9.320872 | 1.657892 | 9.320872 | 0.462202 | 9.132053 | -0.675973 | 9.048397 | 0.339334 | 5.268629 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 9.721907 | 9.721907 | 3.637955 | 8.446949 | 0.390354 | 8.922463 | -0.259681 | 0.330876 | 0.406799 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 9.816976 | 2.750347 | 9.816976 | 0.391082 | 9.178747 | -0.797772 | 9.341094 | 0.539482 | 0.975727 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 10.152766 | 10.152766 | 3.485694 | 9.028112 | 0.426306 | 9.313096 | 0.488296 | 1.326138 | 1.359330 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | N04 | digital_ok | ee Shape | 10.037862 | 4.293943 | 10.037862 | 0.382557 | 9.494251 | 0.262817 | 9.403131 | 0.121737 | 1.203496 |